{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TensorFlow version: 2.0.0\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import datetime, os\n",
    "#hide tf logs\n",
    "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'  # or any {'0', '1', '2'},\n",
    "#0 (default) shows all, 1 to filter out INFO logs, 2 to additionally filter out WARNING logs, and 3 to additionally filter out ERROR logs\n",
    "import scipy.optimize\n",
    "import scipy.io\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker\n",
    "import time\n",
    "from pyDOE import lhs         #Latin Hypercube Sampling\n",
    "import pandas as pd\n",
    "import seaborn as sns \n",
    "import codecs, json\n",
    "\n",
    "# generates same random numbers each time\n",
    "np.random.seed(1234)\n",
    "tf.random.set_seed(1234)\n",
    "\n",
    "print(\"TensorFlow version: {}\".format(tf.__version__))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# *Data Prep*\n",
    "\n",
    "Training and Testing data is prepared from the solution file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_1 = np.linspace(-1,1,256)  # 256 points between -1 and 1 [256x1]\n",
    "x_2 = np.linspace(1,-1,256)  # 256 points between 1 and -1 [256x1]\n",
    "\n",
    "X, Y = np.meshgrid(x_1,x_2) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test Data\n",
    "\n",
    "We prepare the test data to compare against the solution produced by the PINN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_u_test = np.hstack((X.flatten(order='F')[:,None], Y.flatten(order='F')[:,None]))\n",
    "\n",
    "# Domain bounds\n",
    "lb = np.array([-1, -1]) #lower bound\n",
    "ub = np.array([1, 1])  #upper bound\n",
    "\n",
    "a_1 = 1 \n",
    "a_2 = 1\n",
    "\n",
    "usol = np.sin(a_1 * np.pi * X) * np.sin(a_2 * np.pi * Y) #solution chosen for convinience  \n",
    "\n",
    "u = usol.flatten('F')[:,None] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def trainingdata(N_u,N_f):\n",
    "    \n",
    "    leftedge_x = np.hstack((X[:,0][:,None], Y[:,0][:,None]))\n",
    "    leftedge_u = usol[:,0][:,None]\n",
    "    \n",
    "    rightedge_x = np.hstack((X[:,-1][:,None], Y[:,-1][:,None]))\n",
    "    rightedge_u = usol[:,-1][:,None]\n",
    "    \n",
    "    topedge_x = np.hstack((X[0,:][:,None], Y[0,:][:,None]))\n",
    "    topedge_u = usol[0,:][:,None]\n",
    "    \n",
    "    bottomedge_x = np.hstack((X[-1,:][:,None], Y[-1,:][:,None]))\n",
    "    bottomedge_u = usol[-1,:][:,None]\n",
    "    \n",
    "    all_X_u_train = np.vstack([leftedge_x, rightedge_x, bottomedge_x, topedge_x])\n",
    "    all_u_train = np.vstack([leftedge_u, rightedge_u, bottomedge_u, topedge_u])  \n",
    "     \n",
    "    #choose random N_u points for training\n",
    "    idx = np.random.choice(all_X_u_train.shape[0], N_u, replace=False) \n",
    "    \n",
    "    X_u_train = all_X_u_train[idx[0:N_u], :] #choose indices from  set 'idx' (x,t)\n",
    "    u_train = all_u_train[idx[0:N_u],:]      #choose corresponding u\n",
    "    \n",
    "    '''Collocation Points'''\n",
    "\n",
    "    # Latin Hypercube sampling for collocation points \n",
    "    # N_f sets of tuples(x,t)\n",
    "    X_f = lb + (ub-lb)*lhs(2,N_f) \n",
    "    X_f_train = np.vstack((X_f, X_u_train)) # append training points to collocation points \n",
    "    \n",
    "    return X_f_train, X_u_train, u_train \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PINN \n",
    "\n",
    "$W \\in \\mathcal{R}^{n_{l-1}\\times{n_l}}$ \n",
    "\n",
    "Creating sequential layers using the $\\textit{class}$ tf.Module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Sequentialmodel(tf.Module): \n",
    "    def __init__(self, layers, name=None):\n",
    "\n",
    "        self.W = []  #Weights and biases\n",
    "        self.parameters = 0 #total number of parameters\n",
    "\n",
    "        for i in range(len(layers)-1):\n",
    "\n",
    "            input_dim = layers[i]\n",
    "            output_dim = layers[i+1]\n",
    "\n",
    "            #Xavier standard deviation \n",
    "            std_dv = np.sqrt((2.0/(input_dim + output_dim)))\n",
    "\n",
    "            #weights = normal distribution * Xavier standard deviation + 0\n",
    "            w = tf.random.normal([input_dim, output_dim], dtype = 'float64') * std_dv\n",
    "\n",
    "            w = tf.Variable(w, trainable=True, name = 'w' + str(i+1))\n",
    "\n",
    "            b = tf.Variable(tf.cast(tf.zeros([output_dim]), dtype = 'float64'), trainable = True, name = 'b' + str(i+1))\n",
    "\n",
    "            self.W.append(w)\n",
    "            self.W.append(b)\n",
    "\n",
    "            self.parameters +=  input_dim * output_dim + output_dim\n",
    "            \n",
    "        self.X = np.zeros(self.parameters) #store iterates\n",
    "        self.G = np.zeros(self.parameters) #store gradients\n",
    "        self.store = np.zeros((max_iter,2)) #store computed values for plotting\n",
    "        self.iter_counter = 0 # iteration counter for optimizer\n",
    "    \n",
    "    def evaluate(self,x):\n",
    "        \n",
    "        #preprocessing input \n",
    "        x = (x - lb)/(ub - lb) #feature scaling\n",
    "        \n",
    "        a = x\n",
    "        \n",
    "        for i in range(len(layers)-2):\n",
    "            \n",
    "            z = tf.add(tf.matmul(a, self.W[2*i]), self.W[2*i+1])\n",
    "            a = tf.nn.tanh(z)\n",
    "            \n",
    "        a = tf.add(tf.matmul(a, self.W[-2]), self.W[-1]) # For regression, no activation to last layer\n",
    "        \n",
    "        return a\n",
    "    \n",
    "    def get_weights(self):\n",
    "\n",
    "        parameters_1d = []  # [.... W_i,b_i.....  ] 1d array\n",
    "        \n",
    "        for i in range (len(layers)-1):\n",
    "            \n",
    "            w_1d = tf.reshape(self.W[2*i],[-1])   #flatten weights \n",
    "            b_1d = tf.reshape(self.W[2*i+1],[-1]) #flatten biases\n",
    "            \n",
    "            parameters_1d = tf.concat([parameters_1d, w_1d], 0) #concat weights \n",
    "            parameters_1d = tf.concat([parameters_1d, b_1d], 0) #concat biases\n",
    "        \n",
    "        return parameters_1d\n",
    "        \n",
    "    def set_weights(self,parameters):\n",
    "                \n",
    "        for i in range (len(layers)-1):\n",
    "\n",
    "            shape_w = tf.shape(self.W[2*i]).numpy() # shape of the weight tensor\n",
    "            size_w = tf.size(self.W[2*i]).numpy() #size of the weight tensor \n",
    "            \n",
    "            shape_b = tf.shape(self.W[2*i+1]).numpy() # shape of the bias tensor\n",
    "            size_b = tf.size(self.W[2*i+1]).numpy() #size of the bias tensor \n",
    "                        \n",
    "            pick_w = parameters[0:size_w] #pick the weights \n",
    "            self.W[2*i].assign(tf.reshape(pick_w,shape_w)) # assign  \n",
    "            parameters = np.delete(parameters,np.arange(size_w),0) #delete \n",
    "            \n",
    "            pick_b = parameters[0:size_b] #pick the biases \n",
    "            self.W[2*i+1].assign(tf.reshape(pick_b,shape_b)) # assign \n",
    "            parameters = np.delete(parameters,np.arange(size_b),0) #delete \n",
    "\n",
    "            \n",
    "    def loss_BC(self,x,y):\n",
    "\n",
    "        loss_u = tf.reduce_mean(tf.square(y-self.evaluate(x)))\n",
    "        return loss_u\n",
    "\n",
    "    def loss_PDE(self, x_to_train_f):\n",
    "    \n",
    "        g = tf.Variable(x_to_train_f, dtype = 'float64', trainable = False)\n",
    "\n",
    "        k = 1    \n",
    "\n",
    "        x_1_f = g[:,0:1]\n",
    "        x_2_f = g[:,1:2]\n",
    "\n",
    "        with tf.GradientTape(persistent=True) as tape:\n",
    "\n",
    "            tape.watch(x_1_f)\n",
    "            tape.watch(x_2_f)\n",
    "\n",
    "            g = tf.stack([x_1_f[:,0], x_2_f[:,0]], axis=1)\n",
    "\n",
    "            u = self.evaluate(g)\n",
    "            u_x_1 = tape.gradient(u,x_1_f)\n",
    "            u_x_2 = tape.gradient(u,x_2_f)\n",
    "\n",
    "        u_xx_1 = tape.gradient(u_x_1,x_1_f)\n",
    "        u_xx_2 = tape.gradient(u_x_2,x_2_f)\n",
    "\n",
    "        del tape\n",
    "\n",
    "        q = -( (a_1*np.pi)**2 + (a_2*np.pi)**2 - k**2 ) * np.sin(a_1*np.pi*x_1_f) * np.sin(a_2*np.pi*x_2_f)\n",
    "\n",
    "        f = u_xx_1 + u_xx_2 + k**2 * u - q #residual\n",
    "\n",
    "        loss_f = tf.reduce_mean(tf.square(f))\n",
    "\n",
    "        return loss_f, f\n",
    "    \n",
    "    def loss(self,x,y,g):\n",
    "\n",
    "        loss_u = self.loss_BC(x,y)\n",
    "        loss_f, f = self.loss_PDE(g)\n",
    "\n",
    "        loss = loss_u + loss_f\n",
    "\n",
    "        return loss, loss_u, loss_f \n",
    "    \n",
    "    def optimizerfunc(self,parameters):\n",
    "        \n",
    "        self.set_weights(parameters)\n",
    "       \n",
    "        with tf.GradientTape() as tape:\n",
    "            tape.watch(self.trainable_variables)\n",
    "            \n",
    "            loss_val, loss_u, loss_f = self.loss(X_u_train, u_train, X_f_train)\n",
    "            \n",
    "        grads = tape.gradient(loss_val,self.trainable_variables)\n",
    "                \n",
    "        del tape\n",
    "        \n",
    "        grads_1d = [ ] #store 1d grads \n",
    "        \n",
    "        for i in range (len(layers)-1):\n",
    "\n",
    "            grads_w_1d = tf.reshape(grads[2*i],[-1]) #flatten weights \n",
    "            grads_b_1d = tf.reshape(grads[2*i+1],[-1]) #flatten biases\n",
    "\n",
    "            grads_1d = tf.concat([grads_1d, grads_w_1d], 0) #concat grad_weights \n",
    "            grads_1d = tf.concat([grads_1d, grads_b_1d], 0) #concat grad_biases\n",
    "        \n",
    "        return loss_val.numpy(), grads_1d.numpy()\n",
    "    \n",
    "    def optimizer_callback(self,parameters):\n",
    "                \n",
    "        loss_value, loss_u, loss_f = self.loss(X_u_train, u_train, X_f_train)\n",
    "        \n",
    "        u_pred = self.evaluate(X_u_test)\n",
    "        error_vec = np.linalg.norm((u-u_pred),2)/np.linalg.norm(u,2)\n",
    "        \n",
    "        tf.print(loss_value, loss_u, loss_f, error_vec)\n",
    "        \n",
    "        self.LbfgsInvHessProduct(parameters)\n",
    "        \n",
    "    def LbfgsInvHessProduct(self,parameters):\n",
    "\n",
    "        self.iter_counter += 1  #update iteration counter \n",
    "\n",
    "        x_k = parameters  \n",
    "\n",
    "        self.X = np.vstack((x_k.T,self.X)) #stack latest value on top row\n",
    "\n",
    "        _,g_k = self.optimizerfunc(parameters) #obtain grads and loss value\n",
    "\n",
    "        self.G = np.vstack((g_k.T,self.G)) #stack latest grads on top row\n",
    "\n",
    "        n_corrs = min(self.iter_counter, maxcor) #for iterations < maxcor, we will take all available updates\n",
    "        \n",
    "        sk = self.X = self.X[:n_corrs] #select top 'n_corrs' x values, with latest value on top by construction\n",
    "        yk = self.G = self.G[:n_corrs] #select top 'n_corrs' gradient values, with latest value on top by construction \n",
    "\n",
    "        #linear operator B_k_inv    \n",
    "        hess_inv = scipy.optimize.LbfgsInvHessProduct(sk,yk) #instantiate class\n",
    "\n",
    "        p_k = - hess_inv.matvec(g_k) #p_k = -B_k_inv * g_k\n",
    "\n",
    "        gkpk = np.dot(p_k,g_k) #term 1 in report\n",
    "\n",
    "        norm_p_k_sq = (np.linalg.norm(p_k,ord=2))**2 # norm squared\n",
    "               \n",
    "        #store the values\n",
    "        self.store[self.iter_counter-1] = [gkpk,norm_p_k_sq]\n",
    "\n",
    "        def ls_function(x):\n",
    "            val, _ = self.optimizerfunc(x)\n",
    "            return val\n",
    "\n",
    "        def ls_gradient(x):\n",
    "            _, grad = self.optimizerfunc(x)\n",
    "            return grad\n",
    "\n",
    "        alpha, _, _, fnewval, _, _ = scipy.optimize.line_search(ls_function, ls_gradient, x_k, p_k, gfk = g_k, maxiter = 50, c1=1e-4, c2=0.9)\n",
    "        \n",
    "        \n",
    "        \"\"\"\n",
    "        Class\n",
    "        -------------\n",
    "\n",
    "        class scipy.optimize.LbfgsInvHessProduct(*args, **kwargs)\n",
    "\n",
    "        sk = array_like, shape=(n_corr, n)\n",
    "        Array of n_corr most recent updates to the solution vector.\n",
    "\n",
    "        yk = array_like, shape=(n_corr, n)\n",
    "        Array of n_corr most recent updates to the gradient.\n",
    "\n",
    "        Methods\n",
    "        -------------\n",
    "\n",
    "        __call__(self, x)  Call self as a function.\n",
    "\n",
    "        adjoint(self)      Hermitian adjoint.\n",
    "\n",
    "        dot(self, x)       Matrix-matrix or matrix-vector multiplication.\n",
    "\n",
    "        matmat(self, X)    Matrix-matrix multiplication.\n",
    "\n",
    "        matvec(self, x)    Matrix-vector multiplication.\n",
    "\n",
    "        rmatmat(self, X)   Adjoint matrix-matrix multiplication.\n",
    "\n",
    "        rmatvec(self, x)   Adjoint matrix-vector multiplication.\n",
    "\n",
    "        todense(self)      Return a dense array representation of this operator.\n",
    "\n",
    "        transpose(self)    Transpose this linear operator.\n",
    "\n",
    "        \"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# *Loss Function*\n",
    "\n",
    "The loss function consists of two parts:\n",
    "1. **loss_BC**: MSE error of boundary losses\n",
    "2. **loss_PDE**: MSE error of collocation points satisfying the PDE\n",
    "\n",
    "**loss** = loss_BC + loss_PDE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 1.28\n",
      "      fun: 6720.459687080752\n",
      " hess_inv: <5301x5301 LbfgsInvHessProduct with dtype=float64>\n",
      "      jac: array([ 0.01212822, -0.00211429,  0.0042545 , ..., -0.00230538,\n",
      "       -0.01096651, -0.01161386])\n",
      "  message: b'STOP: TOTAL NO. of ITERATIONS REACHED LIMIT'\n",
      "     nfev: 3\n",
      "      nit: 1\n",
      "     njev: 3\n",
      "   status: 1\n",
      "  success: False\n",
      "        x: array([ 0.35016092, -0.21633224, -0.13545744, ..., -0.0493473 ,\n",
      "       -0.12345193,  0.03807622])\n",
      "Test Error: nan\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\anaconda\\envs\\tf2\\lib\\site-packages\\ipykernel_launcher.py:61: MatplotlibDeprecationWarning: shading='flat' when X and Y have the same dimensions as C is deprecated since 3.3.  Either specify the corners of the quadrilaterals with X and Y, or pass shading='auto', 'nearest' or 'gouraud', or set rcParams['pcolor.shading'].  This will become an error two minor releases later.\n",
      "c:\\anaconda\\envs\\tf2\\lib\\site-packages\\ipykernel_launcher.py:69: MatplotlibDeprecationWarning: shading='flat' when X and Y have the same dimensions as C is deprecated since 3.3.  Either specify the corners of the quadrilaterals with X and Y, or pass shading='auto', 'nearest' or 'gouraud', or set rcParams['pcolor.shading'].  This will become an error two minor releases later.\n",
      "c:\\anaconda\\envs\\tf2\\lib\\site-packages\\ipykernel_launcher.py:77: MatplotlibDeprecationWarning: shading='flat' when X and Y have the same dimensions as C is deprecated since 3.3.  Either specify the corners of the quadrilaterals with X and Y, or pass shading='auto', 'nearest' or 'gouraud', or set rcParams['pcolor.shading'].  This will become an error two minor releases later.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "N_u = 400 #Total number of data points for 'u'\n",
    "N_f = 10000 #Total number of collocation points \n",
    "\n",
    "# Training data\n",
    "X_f_train, X_u_train, u_train = trainingdata(N_u,N_f)\n",
    "\n",
    "layers = np.array([2, 50, 50, 50, 1]) #3 hidden layers\n",
    "\n",
    "maxcor = 200 \n",
    "max_iter = 5000\n",
    "\n",
    "PINN = Sequentialmodel(layers)\n",
    "\n",
    "# #load saved weights \n",
    "# new_checkpoint = tf.train.Checkpoint(model = PINN)   # get checkpoint of new model\n",
    "# chkp_path = \"Non_Stiff_problem/my_checkpoint\"                # define stored path\n",
    "# new_checkpoint.restore(chkp_path)                    # overwrite new model variables \n",
    "\n",
    "init_params = PINN.get_weights().numpy()\n",
    "\n",
    "start_time = time.time() \n",
    "\n",
    "# train the model with Scipy L-BFGS optimizer\n",
    "results = scipy.optimize.minimize(fun = PINN.optimizerfunc, \n",
    "                                  x0 = init_params, \n",
    "                                  args=(), \n",
    "                                  method='L-BFGS-B', \n",
    "                                  jac= True,        # If jac is True, fun is assumed to return the gradient along with the objective function\n",
    "                                  callback = None, \n",
    "                                  options = {'disp': None,\n",
    "                                            'maxcor': maxcor, \n",
    "                                            'ftol': 1 * np.finfo(float).eps,  #The iteration stops when (f^k - f^{k+1})/max{|f^k|,|f^{k+1}|,1} <= ftol\n",
    "                                            'gtol': 5e-10, \n",
    "                                            'maxfun':  50000, \n",
    "                                            'maxiter': 1,\n",
    "                                            'iprint': -1,   #print update every 50 iterations\n",
    "                                            'maxls': 50})\n",
    "\n",
    "elapsed = time.time() - start_time                \n",
    "print('Training time: %.2f' % (elapsed))\n",
    "\n",
    "print(results)\n",
    "\n",
    "# np.savetxt('values_stiff.txt', PINN.store)\n",
    "\n",
    "PINN.set_weights(results.x)\n",
    "\n",
    "''' Model Accuracy ''' \n",
    "u_pred = PINN.evaluate(X_u_test)\n",
    "\n",
    "error_vec = np.linalg.norm((u-u_pred),2)/np.linalg.norm(u,2)        # Relative L2 Norm of the error (Vector)\n",
    "print('Test Error: %.5f'  % (error_vec))\n",
    "\n",
    "u_pred = np.reshape(u_pred,(256,256),order='F') \n",
    "\n",
    "# Plotting\n",
    "\n",
    "#Ground truth\n",
    "fig_1 = plt.figure(1, figsize=(18, 5))\n",
    "plt.subplot(1, 3, 1)\n",
    "plt.pcolor(x_1, x_2, usol, cmap='jet')\n",
    "plt.colorbar()\n",
    "plt.xlabel(r'$x_1$', fontsize=18)\n",
    "plt.ylabel(r'$x_2$', fontsize=18)\n",
    "plt.title('Ground Truth $u(x_1,x_2)$', fontsize=15)\n",
    "\n",
    "# Prediction\n",
    "plt.subplot(1, 3, 2)\n",
    "plt.pcolor(x_1, x_2, u_pred, cmap='jet')\n",
    "plt.colorbar()\n",
    "plt.xlabel(r'$x_1$', fontsize=18)\n",
    "plt.ylabel(r'$x_2$', fontsize=18)\n",
    "plt.title('Predicted $\\hat u(x_1,x_2)$', fontsize=15)\n",
    "\n",
    "# Error\n",
    "plt.subplot(1, 3, 3)\n",
    "plt.pcolor(x_1, x_2, np.abs(usol - u_pred), cmap='jet')\n",
    "plt.colorbar()\n",
    "plt.xlabel(r'$x_1$', fontsize=18)\n",
    "plt.ylabel(r'$x_2$', fontsize=18)\n",
    "plt.title(r'Absolute error $|u(x_1,x_2)- \\hat u(x_1,x_2)|$', fontsize=15)\n",
    "plt.tight_layout()\n",
    "\n",
    "fig.savefig('Helmholtz_non_stiff.png', dpi = 500, bbox_inches='tight')\n",
    "\n",
    "# #Residual plot\n",
    "# loss_f, f_plot = PINN.loss_PDE(X_u_test)\n",
    "# plt.scatter(X_u_test[:,0:1], X_u_test[:,1:2], c=f_plot, cmap = 'jet')\n",
    "# plt.axis('scaled')\n",
    "# plt.colorbar()\n",
    "# plt.savefig('Stiff_Helmholtz_residual.png', dpi = 500)\n",
    "\n",
    "# #plot gkpk\n",
    "# plt.semilogy(PINN.store[:,0])\n",
    "# plt.yscale('symlog')\n",
    "# plt.savefig('gkpk_stiff.png', dpi = 500)\n",
    "\n",
    "# #plot norm_p_k_sq\n",
    "# plt.semilogy(PINN.store[:,1])\n",
    "# plt.yscale('symlog')\n",
    "# plt.savefig('norm_p_k_sq_stiff.png', dpi = 500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot of collocation points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "N_u = 400 #Total number of data points for 'u'\n",
    "N_f = 10000 #Total number of collocation points \n",
    "\n",
    "# Training data\n",
    "X_f_train, X_u_train, u_train = trainingdata(N_u,N_f)\n",
    "\n",
    "fig,ax = plt.subplots()\n",
    "\n",
    "plt.plot(X_u_train[:,0],X_u_train[:,1], '*', color = 'red', markersize = 5, label = 'Boundary collocation points= 400')\n",
    "plt.plot(X_f_train[:,0],X_f_train[:,1], 'o', markersize = 0.5, label = 'PDE collocation points = 10,000')\n",
    "\n",
    "plt.xlabel(r'$x_1$')\n",
    "plt.ylabel(r'$x_2$')\n",
    "plt.title('Collocation points')\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')\n",
    "plt.axis('scaled')\n",
    "plt.show()\n",
    "\n",
    "# fig.savefig('collocation_points_Helmholtz.png', dpi = 500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Transfer Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Non_stiff/my_checkpoint'"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chkp_path = \"Stiff_problem/my_checkpoint\"  #path to store file\n",
    "checkpoint = tf.train.Checkpoint(model = PINN) #tf.Module can be saved as checkpoint\n",
    "checkpoint.write(chkp_path)                    #write to path "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('_CHECKPOINTABLE_OBJECT_GRAPH', []),\n",
       " ('model/W/0/.ATTRIBUTES/VARIABLE_VALUE', [2, 50]),\n",
       " ('model/W/1/.ATTRIBUTES/VARIABLE_VALUE', [50]),\n",
       " ('model/W/2/.ATTRIBUTES/VARIABLE_VALUE', [50, 50]),\n",
       " ('model/W/3/.ATTRIBUTES/VARIABLE_VALUE', [50]),\n",
       " ('model/W/4/.ATTRIBUTES/VARIABLE_VALUE', [50, 50]),\n",
       " ('model/W/5/.ATTRIBUTES/VARIABLE_VALUE', [50]),\n",
       " ('model/W/6/.ATTRIBUTES/VARIABLE_VALUE', [50, 1]),\n",
       " ('model/W/7/.ATTRIBUTES/VARIABLE_VALUE', [1])]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.train.list_variables(chkp_path)             #look inside checkpoint and ensure everything is saved"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with tf.GradientTape(persistent=True) as tape:\n",
    "    loss_value, loss_u, loss_f = PINN.loss(X_u_train, u_train, X_f_train)\n",
    "    grad_u = tape.gradient(loss_u, PINN.trainable_variables) \n",
    "    grad_f = tape.gradient(loss_f, PINN.trainable_variables)\n",
    "    del tape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Writing gradients to 2 separate JSON files \n",
    "\n",
    "L = len(layers)-1 #number of weights matrices\n",
    "\n",
    "for i in range (L*2):\n",
    "\n",
    "    temp = grad_f[i].numpy().tolist() # nested lists with same data, indices\n",
    "    json.dump(temp, codecs.open(\"Stiff_problem/Gradients/gradients_f\" + str(i) + \".json\", 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=0)\n",
    "    \n",
    "    temp = grad_u[i].numpy().tolist() # nested lists with same data, indices\n",
    "    json.dump(temp, codecs.open(\"Stiff_problem/Gradients/gradients_u\" + str(i) + \".json\", 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "f_1 = {} #residual non-stiff problem\n",
    "u_1 = {} #boundary non-stiff problem\n",
    "\n",
    "f_2 = {} #residual stiff problem\n",
    "u_2 = {} #boundary stiff problem\n",
    "\n",
    "L = 4\n",
    "\n",
    "#Reading gradients from JSON files and storing in a dict \n",
    "#storing gradients of both weights and biases\n",
    "\n",
    "for i in range (L):\n",
    "    \n",
    "    #weights and biases\n",
    "#     obj_text_w = codecs.open(\"Non_Stiff_problem/Gradients/gradients_f\" + str(2*i) + \".json\", 'r', encoding='utf-8').read()\n",
    "#     obj_text_b = codecs.open(\"Non_Stiff_problem/Gradients/gradients_f\" + str(2*i+1) + \".json\", 'r', encoding='utf-8').read()\n",
    "#     f_1['f'+ str(i)] = np.concatenate((np.array(json.loads(obj_text_w)).flatten('F'), np.array(json.loads(obj_text_b)).flatten('F')))\n",
    "    \n",
    "#     obj_text_w = codecs.open(\"Non_Stiff_problem/Gradients/gradients_u\" + str(2*i) + \".json\", 'r', encoding='utf-8').read()\n",
    "#     obj_text_b = codecs.open(\"Non_Stiff_problem/Gradients/gradients_u\" + str(2*i+1) + \".json\", 'r', encoding='utf-8').read()\n",
    "#     u_1['u'+ str(i)] = np.concatenate((np.array(json.loads(obj_text_w)).flatten('F'), np.array(json.loads(obj_text_b)).flatten('F')))\n",
    "    \n",
    "    obj_text_w = codecs.open(\"Stiff_problem/Gradients/gradients_f\" + str(2*i) + \".json\", 'r', encoding='utf-8').read()\n",
    "    obj_text_b = codecs.open(\"Stiff_problem/Gradients/gradients_f\" + str(2*i+1) + \".json\", 'r', encoding='utf-8').read()\n",
    "    f_2['f'+ str(i)] = np.concatenate((np.array(json.loads(obj_text_w)).flatten('F'), np.array(json.loads(obj_text_b)).flatten('F')))\n",
    "    \n",
    "    obj_text_w = codecs.open(\"Stiff_problem/Gradients/gradients_u\" + str(2*i) + \".json\", 'r', encoding='utf-8').read()\n",
    "    obj_text_b = codecs.open(\"Stiff_problem/Gradients/gradients_u\" + str(2*i+1) + \".json\", 'r', encoding='utf-8').read()\n",
    "    u_2['u'+ str(i)] = np.concatenate((np.array(json.loads(obj_text_w)).flatten('F'), np.array(json.loads(obj_text_b)).flatten('F')))\n",
    "    \n",
    "    \n",
    "#     #only weights\n",
    "#     obj_text_w = codecs.open(\"Non_Stiff_problem/Gradients/gradients_f\" + str(2*i) + \".json\", 'r', encoding='utf-8').read()\n",
    "#     f_1['f'+ str(i)] = np.array(json.loads(obj_text_w)).flatten('F')\n",
    "    \n",
    "#     obj_text_w = codecs.open(\"Non_Stiff_problem/Gradients/gradients_u\" + str(2*i) + \".json\", 'r', encoding='utf-8').read()\n",
    "#     u_1['u'+ str(i)] = np.array(json.loads(obj_text_w)).flatten('F')\n",
    "    \n",
    "#     obj_text_w = codecs.open(\"Stiff_problem/Gradients/gradients_f\" + str(2*i) + \".json\", 'r', encoding='utf-8').read()\n",
    "#     f_2['f'+ str(i)] = np.array(json.loads(obj_text_w)).flatten('F')\n",
    "    \n",
    "#     obj_text_w = codecs.open(\"Stiff_problem/Gradients/gradients_u\" + str(2*i) + \".json\", 'r', encoding='utf-8').read()\n",
    "#     u_2['u'+ str(i)] = np.array(json.loads(obj_text_w)).flatten('F')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Plot the gradients\n",
    "#seaborn = 0.9.0\n",
    "\n",
    "cnt = 1\n",
    "fig = plt.figure(4, figsize=(13, 4))\n",
    "\n",
    "for i in range (L):\n",
    "    \n",
    "    ax = plt.subplot(1, 4, cnt)\n",
    "    ax.set_title('Layer {}'.format(i + 1))\n",
    "    ax.set_yscale('symlog')\n",
    "    \n",
    "    gradients_res = f_2['f'+ str(i)]\n",
    "    gradients_bcs = u_2['u'+ str(i)]\n",
    "      \n",
    "    sns.distplot(gradients_bcs, hist=False,\n",
    "                 kde_kws={\"shade\": False},\n",
    "                 norm_hist=False, label=r'$\\nabla_{\\theta} \\hat J_{BC}$')\n",
    "    \n",
    "    sns.distplot(gradients_res, hist=False,\n",
    "                 kde_kws={\"shade\": False},\n",
    "                 norm_hist=False, label=r'$\\nabla_{\\theta} \\hat J_{PDE}$')\n",
    "\n",
    "    ax.get_legend().remove()\n",
    "    ax.set_xlim([-1e-1, 1e-1])\n",
    "    ax.set_ylim([0,1e6])\n",
    "    cnt += 1\n",
    "    \n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "fig.legend(handles, labels, loc=\"upper left\", bbox_to_anchor=(0.35, -0.01),\n",
    "             borderaxespad=0, bbox_transform=fig.transFigure, ncol=2)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# fig.savefig('Gradient_histogram_stiff.png', dpi = 500, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data = np.loadtxt(\"Stiff_problem/convergence_history.txt\", comments = \"#\", delimiter = \" \", unpack = False)\n",
    "\n",
    "# fig,ax = plt.subplots()\n",
    "# plt.semilogy(np.arange(1,5001),data[:,0], color = 'blue', label = '$\\hat J_{PINN} = \\hat J_{BC} + \\hat J_{PDE}$')\n",
    "# plt.semilogy(np.arange(1,5001),data[:,1], color = 'green', label = ' $\\hat J_{BC}$')\n",
    "# plt.semilogy(np.arange(1,5001),data[:,2], color = 'orange', label = '$\\hat J_{PDE}$')\n",
    "# plt.semilogy(np.arange(1,5001),data[:,3], color = 'red', label = '$\\epsilon_{PINN}$')\n",
    "\n",
    "# ax.set_yticks([1e4, 1e3, 1e2, 1e1, 1e0, 1e-1, 1e-2, 1e-3])\n",
    "\n",
    "# plt.legend()\n",
    "# ax.set_xlabel('iterations')\n",
    "# ax.set_ylabel('Loss/Error')\n",
    "# plt.grid()\n",
    "# plt.show()\n",
    "\n",
    "# # fig.savefig('Stiff_Helmholtz_convergence_history.eps', format = 'eps')\n",
    "# # fig.savefig('Stiff_Helmholtz_convergence_history.png', dpi = 500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# B =  results.hess_inv.todense() #hessian estimate\n",
    "\n",
    "# B = tf.convert_to_tensor(B, dtype = tf.float32)\n",
    "\n",
    "# v = tf.linalg.eigvalsh(B)\n",
    "\n",
    "# np.savetxt(\"stiff_eig_maxcor_100.txt\", v)\n",
    "\n",
    "# plt.semilogy(v)\n",
    "# plt.savefig('stiff_eig_maxcor_100.png', dpi = 500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# s1 = np.loadtxt('values_non_stiff.txt', comments = \"#\", delimiter = \" \", unpack = False)\n",
    "# s2 = np.loadtxt('values_stiff.txt', comments = \"#\", delimiter = \" \", unpack = False)\n",
    "\n",
    "# #plot norm_p_k_sq\n",
    "# fig,ax = plt.subplots()\n",
    "# plt.semilogy(s1[:,1], label = 'Non-stiff')\n",
    "# plt.semilogy(s2[:,1], label = 'Stiff')\n",
    "# plt.yscale('symlog')\n",
    "# plt.xlabel('iterations')\n",
    "# plt.ylabel('$||p_k||^2_2$')\n",
    "# plt.legend()\n",
    "# plt.show()\n",
    "\n",
    "# # fig.savefig('norm_p_k_sq_compare.png', dpi = 500, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# s1 = np.loadtxt('values_non_stiff.txt', comments = \"#\", delimiter = \" \", unpack = False)\n",
    "# s2 = np.loadtxt('values_stiff.txt', comments = \"#\", delimiter = \" \", unpack = False)\n",
    "\n",
    "# #plot g_k_p_k\n",
    "# fig,ax = plt.subplots()\n",
    "# plt.semilogy(s1[:,0], label = 'Non-stiff')\n",
    "# plt.semilogy(s2[:,0], label = 'Stiff')\n",
    "# plt.yscale('symlog')\n",
    "# plt.xlabel('iterations')\n",
    "# plt.ylabel('$g_k^Tp_k$')\n",
    "# plt.legend()\n",
    "# plt.show()\n",
    "\n",
    "# # fig.savefig('g_k_p_k_compare.png', dpi = 500, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Save Weights\n",
    "[See Documentation](https://www.tensorflow.org/guide/intro_to_modules#saving_weights) or (see comments below)\n",
    "\n",
    "<!-- \n",
    "\n",
    "chkp_path = \"Non_Stiff_problem/my_checkpoint\"        # path to store file\n",
    "checkpoint = tf.train.Checkpoint(model = PINN)       # tf.Module can be saved as checkpoint\n",
    "checkpoint.write(chkp_path)                          # write to path \n",
    "\n",
    "tf.train.list_variables(chkp_path)                   # look inside checkpoint and ensure everything is saved\n",
    "\n",
    "PINN_2 = Sequentialmodel(layers)                     # instantiate new model \n",
    "new_checkpoint = tf.train.Checkpoint(model = PINN_2) # get checkpoint of new model\n",
    "chkp_path = \"Non_stiff/my_checkpoint\"                # define stored path\n",
    "new_checkpoint.restore(chkp_path)                    # overwrite variables  \n",
    "\n",
    "-->"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
